Machine learning predicted emission of water-stable CdTe quantum dots

Abstract

Quantum dots (QDs) have attracted much attention and exhibit many attractive properties, including high absorption coefficient, adjustable bandgap, high brightness, long-term stability, and size-dependent emission. It is known that to obtain high-quality luminescent properties (i.e. emission color, color purity, quantum yield, and stability), the synthesis parameters must be precisely controlled. In this work, we have constructed a database with CdTe aqueous synthesis parameters and spectroscopic results and applied machine learning algorithms to better understand the influence of the main synthesis parameters of CdTe QDs on their final emission properties. A strong dependence of the final emission wavelength with the reaction time and surface ligands and precursors concentrations was demonstrated. These parameters adjusted synchronously were shown to be very useful for provide ideal synthesis conditions for the preparation of CdTe QDs with desirable emission wavelengths. Moreover, applying the algorithms correctly allows for obtaining information and insights into the growth kinetics of QDs under different synthetic conditions.